The constantly growing quantity of available digital data requires novel methods in order to enable direct access to relevant information. The automatic extraction of pictorial information from digital images plays a central role in this context. The traditional approach - the manual annotation of the images' content by means of alphanumeric text - has proven in the past as too error-prone and too cost-intensive. In the scope of this work an alternative approach that enables the handling of large quantities of digital image data by means of feature-based algorithms is pursuit. It is assumed that the images are not constrained by their visual appearance or their semantic content. For this purpose a generic model for feature-based image retrieval is defined. The model constitutes a formal framework for the development and combination of new algorithms for feature extraction and similarity indexing. It enables content-based retrieval systems to be built on basis of a uniform software architecture with standardized interfaces. Such a system can be easily extended by individually developed components with regard to customer-specific problems. Human visual recognition and recall are highly dependent on color as a visual cue. A key step of the feature extraction process is the meaningful interpretation of individual pixel colors. Contrary to the potential of technical representations, humans can only differentiate between a few different colors. Therefore, a novel color model is introduced that makes color information accessible by means of - for humans meaningful - color names. Its underlying mathematical foundation enables a simple and fast comparison of colors. Such a representation of color can be leveraged profitably for almost any task in the domain of Digital Image Processing. Some feature extraction algorithms based on this color model that describe certain visual aspects of a digital image are presented exemplarily. Large amounts of data inherently require longer processing times when performing search tasks. Consequently, a similarity indexing scheme is presented that breaks the proportional relationship between the amount of data searched and the time needed to perform the search. The indexing scheme is based on the principle of localization of the search in feature space. A significant acceleration can be achieved by reducing the search space to the immediate neighborhood of the query. Since such an approach is immanently afflicted with a certain imprecision, experimental results are presented that document its usefulness in a realistic scenario.